Multi-Scale Aerosol Observations, Property Retrieval, Model Development, and AI-Based Forecasting: From Regional to Global Perspectives

The past decades have seen substantial progress in aerosol observations. Ground-based networks such as AERONET provide long-term, high-quality columnar aerosol properties. Meanwhile, satellite-based instruments provide measurements of aerosol optical depth and, to some extent, information on aerosol composition and vertical structure across various spatiotemporal scales. Concurrently, chemical transport models have grown increasingly sophisticated in simulating aerosol lifecycle processes. Despite these advances, notable gaps persist between observational capabilities and model simulation requirements. For instance, satellite retrievals remain challenging over bright surfaces and under cloudy conditions; models continue to face difficulties in accurately estimate emissions and representing vertical mixing; and the reliability of converting aerosol optical measurements to surface concentrations still remains uncertain. Addressing these gaps requires coordinated efforts that integrate multi-source observations with modeling tools.Atmospheric aerosols are among the most influential components of the Earth system, with significant effects on climate, clouds, and air quality. However, large uncertainties remain in quantifying their distributions, properties, and environmental impacts. These uncertainties stem largely from the multi-scale nature of aerosols (i.e., sources, transformations, and transport) span local to global scales, while observations and models are often limited to specific spatial or temporal domains.This Research Topic promotes integrated approaches linking multi-scale observations with model development. We encourage contributions combining ground-based networks, field campaigns, and satellite data to improve understanding of aerosol physical and optical properties. Studies using observational constraints to refine emission inventories, transport mechanisms, and removal processes in models are also welcome. With the rise of artificial intelligence, integrating AI techniques with physics-based models to predict aerosol microphysical and optical properties has attracted growing research interest. By fostering collaboration across disciplines, we aim to address persistent challenges and reduce uncertainties in aerosol characterization, modeling and forecasting.This Research Topic welcomes contributions that advance the measurement, retrieval, modeling, and application of aerosol information across scales. We encourage submissions that address one or more of the following themes: 1) Integrated aerosol observations: Studies utilizing ground-based, UAV-borne, balloon-borne, shipborne, or satellite measurements to characterize aerosol properties and variability across different spatial and temporal scales, whether through individual platforms or synergistic combinations.2) Retrieval algorithm development: Improvements in retrieval techniques for aerosol optical depth, size distribution, absorption, or vertical profile from satellite or ground-based measurements, particularly in challenging environments such as bright surfaces, cloudy conditions, or complex aerosol mixtures.3) Model evaluation and development: Assessments of model performance (e.g., chemical transport models, size-resolved aerosol models, or other aerosol modeling frameworks) using multi-source observations; improvements in process representations such as emissions, transport, transformation, wet deposition, or aerosol-radiation interactions.4) Aerosol forecasting applications: Development and evaluation of forecasting systems for aerosol events including dust storms, biomass burning plumes, and pollution episodes, using either process-based or statistical approaches.5) Regional to global synthesis: Analyses that bridge local measurements with regional or global assessments to understand aerosol sources, transport pathways, and environmental impacts.We invite Original Research articles, Reviews, Methods papers, and Data Descriptions that contribute to these themes. Topic editors: Shengcheng Cui, Cheng Chen, Xi Chen Source: https://www.frontiersin.org/research-topics/79244/multi-scale-aerosol-observations-property-retrieval-model-development-and-ai-based-forecasting-from-regional-to-global-perspectives